Abstract
In a wind tunnel process, Mach number is the most important parameter. However, it is difficult to measure directly, especially in the multimode operation process, leading to difficulty in process monitoring. Thus, it is necessary to measure the Mach number indirectly by utilizing data-driven methods, and based on which, to monitor the operation status of the wind tunnel process. In this paper, therefore, a multimode wind tunnel flow field system monitoring strategy is proposed. Since the wind tunnel system is a strongly nonlinear system, the kernel partial least squares method, which can efficiently handle the nonlinear regression problem, is utilized. Firstly, the Mach number is predicted utilizing the kernel partial least squares method. Secondly, process monitoring statistics, i.e., the Hotelling T2 statistic and the square prediction error, the SPE statistic, and their control limits, are proposed to be applied to monitor the wind tunnel process on the basis of the prediction of the Mach number. Finally, the Mach number prediction and monitoring strategy are applied to a real process, where mode analysis and division is necessary. After mode division, the single-mode and multimode processes are modeled and predicted, respectively, and both the single-mode and multimode processes are monitored online. Satisfactory results were achieved compared with those of the partial least squares method.